
    Z j^U                        S SK Jr  S SKJr  S SKrS SKJr  SSKJr  SSKJ	r	J
r
  SSKJr  SS	KJrJrJr  SS
KJrJr  SSKJr  SSKJrJrJrJr  SSKJrJr  SSKJrJ r   SSK!J"r"J#r#  SSK$J%r%  SSK&J'r'J(r(J)r)  SSK*J+r+J,r,  SSK-J.r.  SSK/J0r0  S r1\" S5      SBS j5       r2S\Rf                  S\4S\Rf                  4S jr5 SCS\Rl                  S\Rf                  S\Rf                  S \Rf                  S!\Rf                  S-  S"\7S#\7S$\%\'   4S% jjr8S&\Rf                  S'\7S(\4S\Rf                  4S) jr9\" \25       " S* S+\Rl                  5      5       r: " S, S-\Rl                  5      r;\" S.5       " S/ S0\Rl                  5      5       r< " S1 S2\5      r=\( " S3 S4\#5      5       r> " S5 S6\Rl                  5      r?\( " S7 S8\>5      5       r@\( " S9 S:\>\5      5       rA " S; S<\\>5      rB " S= S>\\>5      rC " S? S@\\>5      rD/ SAQrEg)D    )Callable)OptionalN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )Ministral3Configc                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..N   dim)shapetorchcat)xx1x2s      ڃ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/ministral3/modeling_ministral3.pyrotate_halfr0   #   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''    rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )aI  Applies Rotary Position Embedding to the query and key tensors.

Args:
    q (`torch.Tensor`): The query tensor.
    k (`torch.Tensor`): The key tensor.
    cos (`torch.Tensor`): The cosine part of the rotary embedding.
    sin (`torch.Tensor`): The sine part of the rotary embedding.
    unsqueeze_dim (`int`, *optional*, defaults to 1):
        The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
        sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
        that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
        k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
        cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
        the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
    `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
)	unsqueezer0   )qkcossinunsqueeze_dimq_embedk_embeds          r/   apply_rotary_pos_embr<   *   sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr1   hidden_statesn_repreturnc                     U R                   u  p#pEUS:X  a  U $ U SS2SS2SSS2SS24   R                  X#XU5      n U R                  X#U-  XE5      $ )z
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
r"   N)r)   expandreshape)r=   r>   batchnum_key_value_headsslenhead_dims         r/   	repeat_kvrG   D   s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr1   modulequerykeyvalueattention_maskscalingdropoutkwargsc                    [        X R                  5      n[        X0R                  5      n	[        R                  " XR	                  SS5      5      U-  n
Ub  X-   n
[
        R                  R                  U
S[        R                  S9R                  UR                  5      n
[
        R                  R                  XU R                  S9n
[        R                  " X5      nUR	                  SS5      R                  5       nX4$ )Nr&   r   r%   )r(   dtype)ptrainingr"   )rG   num_key_value_groupsr*   matmul	transposer   
functionalsoftmaxfloat32torQ   rN   rS   
contiguous)rH   rI   rJ   rK   rL   rM   rN   rO   
key_statesvalue_statesattn_weightsattn_outputs               r/   eager_attention_forwardr`   P   s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r1   positions_idsbetamax_position_embeddingsc           	          SU[         R                  " S[         R                  " X-  5      -   5      -  -   nUS S 2S S S 2S 4   $ )Nr"   )r*   logfloor)ra   rb   rc   rM   s       r/   get_llama_4_attn_scalerg   i   s?    $1u{{=3Z'[#[\\\G1dAt#$$r1   c                     ^  \ rS rSrSrS\S\4U 4S jjr SS\R                  S\
\R                  \R                  4   S	\R                  S-  S
\R                  S\S-  S\\   S\
\R                  \R                  S-  4   4S jjrSrU =r$ )Ministral3Attentionn   z=Multi-headed attention from 'Attention Is All You Need' paperconfig	layer_idxc                   > [         TU ]  5         Xl        X l        [	        USS 5      =(       d    UR
                  UR                  -  U l        UR                  UR                  -  U l	        U R                  S-  U l
        UR                  U l        SU l        [        R                  " UR
                  UR                  U R                  -  SS9U l        [        R                  " UR
                  UR                  U R                  -  SS9U l        [        R                  " UR
                  UR                  U R                  -  SS9U l        [        R                  " UR                  U R                  -  UR
                  SS9U l        g )NrF   g      TFbias)super__init__rk   rl   getattrhidden_sizenum_attention_headsrF   rD   rT   rM   attention_dropout	is_causalr   Linearq_projk_projv_projo_projselfrk   rl   	__class__s      r/   rq   Ministral3Attention.__init__r   s.   "
D9mV=O=OSYSmSm=m$*$>$>&B\B\$\!}}d*!'!9!9ii 2 2F4N4NQUQ^Q^4^ejkii 2 2F4N4NQUQ^Q^4^ejkii 2 2F4N4NQUQ^Q^4^ejkii : :T]] JFL^L^ejkr1   Nr=   position_embeddingsrL   position_idspast_key_valuesrO   r?   c           
         UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  U5      R                  U5      R	                  SS5      nUu  p[        XX5      u  pU	[        UU R                  R                  R                  S5      U R                  R                  R                  S5      5      R                  U	R                  5      -  n	Ub  UR                  XU R                  5      u  p[         R"                  " U R                  R$                  [&        5      nU" U U	U
UU4U R(                  (       d  SOU R*                  U R,                  [/        U R                  SS 5      S.UD6u  nnUR0                  " / UQSP76 R3                  5       nU R5                  U5      nUU4$ )	Nr%   r"   r&   llama_4_scaling_beta original_max_position_embeddings        sliding_window)rN   rM   r   )r)   rF   rx   viewrV   ry   rz   r<   rg   rk   rope_parametersgetrZ   rQ   updaterl   r   get_interface_attn_implementationr`   rS   ru   rM   rr   rB   r[   r{   )r}   r=   r   rL   r   r   rO   input_shapehidden_shapequery_statesr\   r]   r7   r8   attention_interfacer_   r^   s                    r/   forwardMinistral3Attention.forward   s    $))#2.88b8$--8{{=166|DNNqRST[[/44\BLLQPQR
{{=166|DNNqRST&#7RU#[ #&<KK''++,BCKK''++,NO'
 "\
 	! &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL"4;;0@$G
%
 
%
!\ "));;;;FFHkk+.L((r1   )ru   rk   rF   rv   ry   rl   rT   r{   rx   rM   rz   N)__name__
__module____qualname____firstlineno____doc__r#   intrq   r*   Tensortupler   r   r   r   __static_attributes____classcell__r~   s   @r/   ri   ri   n   s    Gl/ lC l( )--)||-) #5<<#=>-) t+	-)
 ll-) -) -.-) 
u||U\\D00	1-) -)r1   ri   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )Ministral3MLP   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l	        [        UR                     U l        g NFrn   )rp   rq   rk   rs   intermediate_sizer   rw   	gate_projup_proj	down_projr   
hidden_actact_fnr}   rk   r~   s     r/   rq   Ministral3MLP.__init__   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r1   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r   )r   r   r   r   )r}   r,   r   s      r/   r   Ministral3MLP.forward   s6    NN4;;t~~a/@#ADLLQRO#ST	r1   )r   rk   r   r   rs   r   r   )r   r   r   r   rq   r   r   r   r   s   @r/   r   r      s    0 r1   r   RMSNormc                   x   ^  \ rS rSrS
S\SS4U 4S jjjrS\R                  S\R                  4S jrS r	S	r
U =r$ )Ministral3RMSNorm   epsr?   Nc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z0
Ministral3RMSNorm is equivalent to T5LayerNorm
N)rp   rq   r   	Parameterr*   onesweightvariance_epsilon)r}   rs   r   r~   s      r/   rq   Ministral3RMSNorm.__init__   s/     	ll5::k#:; #r1   r=   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )Nr&   r%   T)keepdim)	rQ   rZ   r*   rY   powmeanrsqrtr   r   )r}   r=   input_dtypevariances       r/   r   Ministral3RMSNorm.forward   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r1   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)r   r   r)   r   )r}   s    r/   
extra_reprMinistral3RMSNorm.extra_repr   s*    ))*+6$2G2G1HIIr1   )r   r   )gư>)r   r   r   r   floatrq   r*   r   r   r   r   r   r   s   @r/   r   r      sB    $ $$ $ $;U\\ ;ell ;J Jr1   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )Ministral3DecoderLayer   rk   rl   c                   > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        g )N)rk   rl   r   )rp   rq   rs   ri   	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr|   s      r/   rq   Ministral3DecoderLayer.__init__   sk    !--,FP (01C1CI\I\](9&:L:LRXReRe(f%r1   Nr=   rL   r   r   	use_cacher   rO   r?   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pX-   nUnU R                  U5      nU R                  U5      nX-   nU$ )N)r=   rL   r   r   r   r    )r   r   r   r   )
r}   r=   rL   r   r   r   r   rO   residual_s
             r/   r   Ministral3DecoderLayer.forward   s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r1   )rs   r   r   r   r   )NNNFN)r   r   r   r   r#   r   rq   r*   r   
LongTensorr   boolr   r   r   r   r   r   r   s   @r/   r   r      s    g/ gC g /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r1   r   c                   R    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rSrg	)
Ministral3PreTrainedModel   rk   modelTr   r   )r=   
attentionsr   N)r   r   r   r   r#   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   ri   _can_record_outputsr   r   r1   r/   r   r      sQ    &*#12#4"5N!"&/)r1   r   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )Ministral3RotaryEmbeddingi  inv_freqNrk   c                   > [         TU ]  5         UR                  U l        UR                  U l        Xl        U R
                  R                  S   U l        U R                  nU R                  S:w  a  [        U R                     nU" U R
                  U5      u  o@l
        U R                  SUSS9  U R                  SUR                  5       SS9  g )N	rope_typedefaultr   F)
persistentoriginal_inv_freq)rp   rq   rc   max_seq_len_cachedoriginal_max_seq_lenrk   r   r   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r}   rk   devicerope_init_fnr   r~   s        r/   rq   "Ministral3RotaryEmbedding.__init__  s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr1   r   ztorch.deviceseq_lenr?   ztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	aH  
Computes the inverse frequencies according to the original RoPE implementation
Args:
    config ([`~transformers.PreTrainedConfig`]):
        The model configuration.
    device (`torch.device`):
        The device to use for initialization of the inverse frequencies.
    seq_len (`int`, *optional*):
        The current sequence length. Unused for this type of RoPE.
Returns:
    Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
    post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).

rope_thetarF   Ng      ?r   r&   rQ   )r   rQ   )	r   rr   rs   rt   r*   arangeint64rZ   r   )rk   r   r   baser(   attention_factorr   s          r/   r   9Ministral3RotaryEmbedding.compute_default_rope_parameters$  s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r1   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r%   r"   mpscpuF)device_typeenabledr&   r'   r   )r   r   rA   r)   rZ   r   
isinstancetypestrr   rV   r*   r+   r7   r   r8   rQ   )
r}   r,   r   inv_freq_expandedposition_ids_expandedr  freqsembr7   r8   s
             r/   r   !Ministral3RotaryEmbedding.forwardB  sN    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E'E!((--[`J`ahhmmfkUC&,,.1F1L1L1NNYYZ[]^_E))UN3C'')d444C'')d444C	 D vvAGGv$cff177f&;;; DCs   BF
F#)r   rk   r   r   r   r   )NNN)r   r   r   r   r*   r   r   r#   rq   staticmethodr   r   r   r   r   no_gradr   r   r   r   r   s   @r/   r   r     s    llV/ V V  *.+/"* 4'*(* t* 
~u$	%	* *: ]]_<  <r1   r   c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )Ministral3ModeliR  rk   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )Nr   rk   F)rp   rq   pad_token_idpadding_idx
vocab_sizer   	Embeddingrs   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normr   
rotary_embgradient_checkpointing	post_initr|   s      r/   rq   Ministral3Model.__init__T  s     !.. ++LL):):F<N<NPTP`P`ammHMfNfNfHghHg9#F6Hgh
 &f&8&8f>Q>QR	36B&+# 	 is   C?N	input_idsrL   r   r   inputs_embedsr   rO   r?   c           
         US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      nU R                  R                  c  [        O[        n	U	" U R                  UUUUS9n
UnU R                  XS9nU R                  S U R                  R                    H  nU" U4U
UUUUS.UD6nM     U R!                  U5      n[#        UU(       a  US	9$ S S	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r"   )r   )rk   r%  rL   r   r   )r   )rL   r   r   r   r   )last_hidden_stater   )
ValueErrorr  r	   rk   get_seq_lengthr*   r   r)   r   r4   r   r   r   r   r  r  r  r   )r}   r$  rL   r   r   r%  r   rO   past_seen_tokensmask_functioncausal_maskr=   r   decoder_layers                 r/   r   Ministral3Model.forwardd  sm    -t";<YZZ  --i8M0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L.2kk.H.H.P*Vw#;;')+%
 &"oomoW![[)H4;;+H+HIM)*) /#$7 M J 		-0&+/8O
 	
>B
 	
r1   )r  r!  r  r  r  r   r  )NNNNNN)r   r   r   r   r#   rq   r    r!   r   r*   r   r   r   FloatTensorr   r   r   r   r   r   r   r   s   @r/   r  r  R  s    /     .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r1   r  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )Ministral3ForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr=   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g r   )
rp   rq   r  r   r  r   rw   rs   r2  r"  r   s     r/   rq   Ministral3ForCausalLM.__init__  sU     $V,
 ++yy!3!3V5F5FUS 	r1   Nr$  rL   r   r   r%  labelsr   logits_to_keeprO   r?   c	           
      |   U R                   " SUUUUUUS.U	D6n
U
R                  n[        U[        5      (       a  [	        U* S5      OUnU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U	D6n[        UUU
R                  U
R                  U
R                  S9$ )a  
Example:

```python
>>> from transformers import AutoTokenizer, Ministral3ForCausalLM

>>> model = Ministral3ForCausalLM.from_pretrained("meta-ministral3/Ministral3-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-ministral3/Ministral3-2-7b-hf")

>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")

>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```)r$  rL   r   r   r%  r   N)r4  r7  r  )lossr4  r   r=   r   r   )r   r'  r  r   slicer2  loss_functionrk   r  r   r   r=   r   )r}   r$  rL   r   r   r%  r7  r   r8  rO   outputsr=   slice_indicesr4  r:  s                  r/   r   Ministral3ForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r1   )r2  r   r  )NNNNNNNr   )r   r   r   r   _tied_weights_keys_tp_plan_pp_planrq   r   r   r*   r   r   r   r/  r   r   r   r   r   r   r   r   r   s   @r/   r1  r1    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r1   r1  c                       \ rS rSrSrg) Ministral3ForTokenClassificationi  r   Nr   r   r   r   r   r   r1   r/   rD  rD        r1   rD  c                       \ rS rSrSrg)#Ministral3ForSequenceClassificationi  r   NrE  r   r1   r/   rH  rH    rF  r1   rH  c                       \ rS rSrSrg)Ministral3ForQuestionAnsweringi  r   NrE  r   r1   r/   rJ  rJ    rF  r1   rJ  )r1  rJ  r  r   rH  rD  )r"   )r   )Fcollections.abcr   typingr   r*   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r    utils.output_capturingr!   configuration_ministral3r#   r0   r<   r   r   rG   Moduler   r`   rg   ri   r   r   r   r   r   r  r1  rD  rH  rJ  __all__r   r1   r/   <module>r^     sG   %    ! . ) f f R B  P K F & I I G 5 6( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2%%,, %e %^a %fkfrfr %
 )*>)")) >) +>)BBII   Y'J		 J (J(&7 &R   $><		 ><B F
/ F
 F
R F
5 F
 F
R	'DF_ 		*JLe 		%@B[ 	r1   